or via 3rd-party network client libraries. HDFS is designed for portability across various hardware platforms and for compatibility with a variety of underlying operating systems. The HDFS design introduces portability limitations that result in some performance bottlenecks, since the Java implementation cannot use features that are exclusive to the platform on which HDFS is running. Due to its widespread integration into enterprise-level infrastructure, monitoring HDFS performance at scale has become an increasingly important issue. Monitoring end-to-end performance requires tracking metrics from datanodes, namenodes, and the underlying operating system. There are currently several monitoring platforms to track HDFS performance, including Hortonworks, Cloudera, and Datadog. Other file systems Hadoop works directly with any distributed file system that can be mounted by the underlying operating system by simply using a file:// URL; however, this comes at a price – the loss of locality. To reduce network traffic, Hadoop needs to know which servers are closest to the data, information that Hadoop-specific file system bridges can provide. In May 2011, the list of supported file systems bundled with Apache Hadoop were: HDFS: Hadoop's own rack-aware file system. This is designed to scale to tens of petabytes of storage and runs on top of the file systems of the underlying operating systems. Apache Hadoop Ozone: HDFS-compatible object store targeting optimized for billions of small files. FTP file system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) object storage: This is targeted at clusters hosted on the Amazon Elastic Compute Cloud server-on-demand infrastructure. There is no rack-awareness in this file system, as it is all remote. Windows Azure Storage Blobs (WASB) file system: This is an extension of HDFS that allows distributions of Hadoop to access data in Azure blob stores without moving the data permanently into the cluster. A number of third-party file system bridges have also been written, none of which are currently in Hadoop distributions. However, some commercial distributions of Hadoop ship with an alternative file system as the defaultspecifically IBM and MapR. In 2009, IBM discussed running Hadoop over the IBM General Parallel File System. The source code was published in October 2009. In April 2010, Parascale published the source code to run Hadoop against the Parascale file system. In April 2010, Appistry released a Hadoop file system driver for use with its own CloudIQ Storage product. In June 2010, HP discussed a location-aware IBRIX